Neural networks for the dimensionality reduction of GOME measurement vector in the estimation of ozone profiles

Frate, Fabio Del; Iapaolo, M.; Casadio, Stefano; Godin‐Beekmann, Sophie; Petitdidier, Monique

doi:10.1016/j.jqsrt.2004.07.028

Cited by 42 publications

(13 citation statements)

References 13 publications

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“…Only Global measurement mode data were used, because only this observation mode provides daily global coverage. The 19 wavelengths were selected according to an extended pruning (EP) technique [31]. This technique aims at reducing the dimensionality of an input vector for a NN by retaining only the most informative inputs, i.e., those who have the strongest influence on the NN output.…”

Section: The Omi-toc Nn Algorithmmentioning

confidence: 99%

Tropospheric ozone column retrieval from OMI data by means of neural networks: a validation exercise with ozone soundings over Europe

Noia

Sellitto

Frate

et al. 2013

EURASIP J. Adv. Signal Process.

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The retrieval of the tropospheric ozone column from satellite data is very important for the characterization of tropospheric chemical and physical properties. However, the task of retrieving tropospheric ozone from space has to face with one fundamental difficulty: the contribution of the tropospheric ozone to the measured radiances is overwhelmed by a much stronger stratospheric signal, which has to be reliably filtered. The Tor Vergata University Earth Observation Laboratory has recently addressed this issue by developing a neural network (NN) algorithm for tropospheric ozone retrieval from NASA-Aura Ozone Monitoring Instrument (OMI) data. The performances of this algorithm were proven comparable to those of more consolidated algorithms, such as Tropospheric Ozone Residual and Optimal Estimation. In this article, the results of a validation of this algorithm with measurements performed at six European ozonesonde sites are shown and critically discussed. The results indicate that systematic errors, related to the tropopause pressure, are present in the current version of the algorithm, and that including the tropopause pressure in the NN input vector can compensate for these errors, enhancing the retrieval accuracy significantly.

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Section: The Omi-toc Nn Algorithmmentioning

confidence: 99%

Tropospheric ozone column retrieval from OMI data by means of neural networks: a validation exercise with ozone soundings over Europe

Noia

Sellitto

Frate

et al. 2013

EURASIP J. Adv. Signal Process.

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“…The major requirements of this application concern the data management, the input and output file handling and the update of the metadata catalogue as soon as new ozone profiles are processed. The ES-TS-GOMEVAL test suite validates 7 years of satellite ozone profiles using Lidar observations (Iapaolo et al 2007;del Frate et al 2005). GOME ozone profiles are obtained using an artificial neural network algorithm on the Grid.…”

Section: Es-ts-gomepro and Es-ts-gomeval Test Suitesmentioning

confidence: 99%

Earth science test suites to evaluate grid tools and middleware—examples for grid data access tools

et al. 2009

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The Earth Science (ES) community has three major IT related concerns: Modeling (computing intensive), exploitation of datasets, and production of large shared datasets. All could be accomplishable using Grid. Different Grid middleware solutions exist and are being developed. In DEGREE (Dissemination and Exploitation of Grids in Earth sciencE) the aim was to disseminate and promote uptake of Grid in ES and to create a bridge between ES and Grid communities. DEGREE identified key ES requirements and has disseminated these to Grid projects, evaluated Grid middleware tools and standards regarding ES requirements and provided feedback to Grid developers. In order to convey requirements to the Grid community, test suite specifications were developed. Test suites provide real applications for testing functional and non-functional aspects of Grid, contrary to typical whiteboard tests or use cases. In this paper the GOMEVAL test suite is explained in detail and the results obtained are discussed.

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“…For exploring the information budget of the SCIAMACHY UV/VIS bands, we performed an extended pruning (EP) procedure as described in [6], using the mentioned synthetic database of radiance-TOC pairs. We first selected the radiance data in the range 280-700 nm for the dataset described in section IV-A, and we matched the spectra with the profiles, correspondingly.…”

Section: B Extended Pruningmentioning

confidence: 99%

Dedicated neural networks algorithms for direct estimation of tropospheric ozone from satellite measurements

Sellitto

Burini²,

Frate³

et al. 2007

2007 IEEE International Geoscience and Remote Sensing Symposium

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In this paper we report on the design of a Neural Networks algorithm to retrieve tropospheric ozone information from satellite data. Following a combined radiative transfer model-extended pruning sensitivity analysis for input wavelengths selection, we first made an inversion exercise based on a synthetically produced radiance-tropospheric ozone concentrations database. Starting from the encouraging obtained results, we tested the Net on ESA-ENVISAT SCIAMACHY Level 1b data. A time series of Tropospheric Ozone Columns on some midlatitude sites has been retrieved from the satellite measurements and then compared with collocated and simultaneous ozonesondes reference columns. The inversion results are presented and critically discussed.

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Neural networks for the dimensionality reduction of GOME measurement vector in the estimation of ozone profiles

Cited by 42 publications

References 13 publications

Tropospheric ozone column retrieval from OMI data by means of neural networks: a validation exercise with ozone soundings over Europe

Tropospheric ozone column retrieval from OMI data by means of neural networks: a validation exercise with ozone soundings over Europe

Earth science test suites to evaluate grid tools and middleware—examples for grid data access tools

Dedicated neural networks algorithms for direct estimation of tropospheric ozone from satellite measurements

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